Overview

Dataset statistics

Number of variables11
Number of observations26
Missing cells57
Missing cells (%)19.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory99.1 B

Variable types

Numeric4
Text3
Unsupported1
Categorical3

Dataset

Description제주특별자치도에서 제공하는 무장애여행 관련 관광지별 "위도, 경도, 장소명칭, 장소상세정보, 무장애관광정보"입니다.
Author제주특별자치도 미래성장과
URLhttps://www.jejudatahub.net/data/view/data/725

Alerts

A has constant value ""Constant
info is highly overall correlated with ID and 4 other fieldsHigh correlation
B is highly overall correlated with ID and 4 other fieldsHigh correlation
ID is highly overall correlated with lat and 3 other fieldsHigh correlation
lat is highly overall correlated with ID and 4 other fieldsHigh correlation
lon is highly overall correlated with lat and 2 other fieldsHigh correlation
Point is highly overall correlated with ID and 3 other fieldsHigh correlation
Description has 9 (34.6%) missing valuesMissing
Image has 26 (100.0%) missing valuesMissing
etc has 22 (84.6%) missing valuesMissing
ID has unique valuesUnique
Point has unique valuesUnique
Image is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 19:35:26.386027
Analysis finished2023-12-11 19:35:28.929815
Duration2.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

ID
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.5
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T04:35:29.017058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.25
Q17.25
median13.5
Q319.75
95-th percentile24.75
Maximum26
Range25
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation7.6485293
Coefficient of variation (CV)0.56655772
Kurtosis-1.2
Mean13.5
Median Absolute Deviation (MAD)6.5
Skewness0
Sum351
Variance58.5
MonotonicityStrictly increasing
2023-12-12T04:35:29.181573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 1
 
3.8%
15 1
 
3.8%
26 1
 
3.8%
25 1
 
3.8%
24 1
 
3.8%
23 1
 
3.8%
22 1
 
3.8%
21 1
 
3.8%
20 1
 
3.8%
19 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
1 1
3.8%
2 1
3.8%
3 1
3.8%
4 1
3.8%
5 1
3.8%
6 1
3.8%
7 1
3.8%
8 1
3.8%
9 1
3.8%
10 1
3.8%
ValueCountFrequency (%)
26 1
3.8%
25 1
3.8%
24 1
3.8%
23 1
3.8%
22 1
3.8%
21 1
3.8%
20 1
3.8%
19 1
3.8%
18 1
3.8%
17 1
3.8%

lat
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.244839
Minimum33.244257
Maximum33.245478
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T04:35:29.316583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.244257
5-th percentile33.244285
Q133.244546
median33.24475
Q333.245171
95-th percentile33.245417
Maximum33.245478
Range0.00122099
Interquartile range (IQR)0.0006250175

Descriptive statistics

Standard deviation0.00038753403
Coefficient of variation (CV)1.1656968 × 10-5
Kurtosis-1.3113948
Mean33.244839
Median Absolute Deviation (MAD)0.000354515
Skewness0.18274115
Sum864.36582
Variance1.5018263 × 10-7
MonotonicityNot monotonic
2023-12-12T04:35:29.491702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
33.24523601 2
 
7.7%
33.24547799 1
 
3.8%
33.24465003 1
 
3.8%
33.24460804 1
 
3.8%
33.24455003 1
 
3.8%
33.24447803 1
 
3.8%
33.244257 1
 
3.8%
33.24426102 1
 
3.8%
33.244358 1
 
3.8%
33.24437996 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
33.244257 1
3.8%
33.24426102 1
3.8%
33.244358 1
3.8%
33.24437996 1
3.8%
33.24447803 1
3.8%
33.24450502 1
3.8%
33.244544 1
3.8%
33.24455003 1
3.8%
33.24459102 1
3.8%
33.24460804 1
3.8%
ValueCountFrequency (%)
33.24547799 1
3.8%
33.24541898 1
3.8%
33.24541203 1
3.8%
33.24536802 1
3.8%
33.24523601 2
7.7%
33.24518203 1
3.8%
33.24513601 1
3.8%
33.24511598 1
3.8%
33.24509301 1
3.8%
33.24494599 1
3.8%

lon
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.57109
Minimum126.5699
Maximum126.57336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T04:35:29.688737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.5699
5-th percentile126.57005
Q1126.57059
median126.57097
Q3126.5714
95-th percentile126.57294
Maximum126.57336
Range0.003458
Interquartile range (IQR)0.000812

Descriptive statistics

Standard deviation0.00085619459
Coefficient of variation (CV)6.7645353 × 10-6
Kurtosis1.7662208
Mean126.57109
Median Absolute Deviation (MAD)0.0004165
Skewness1.2516625
Sum3290.8483
Variance7.3306918 × 10-7
MonotonicityNot monotonic
2023-12-12T04:35:29.884557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
126.571064 2
 
7.7%
126.571327 1
 
3.8%
126.573356 1
 
3.8%
126.573229 1
 
3.8%
126.570427 1
 
3.8%
126.570114 1
 
3.8%
126.569898 1
 
3.8%
126.570032 1
 
3.8%
126.570187 1
 
3.8%
126.570635 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
126.569898 1
3.8%
126.570032 1
3.8%
126.570114 1
3.8%
126.570187 1
3.8%
126.570427 1
3.8%
126.570522 1
3.8%
126.570577 1
3.8%
126.570629 1
3.8%
126.570635 1
3.8%
126.570734 1
3.8%
ValueCountFrequency (%)
126.573356 1
3.8%
126.573229 1
3.8%
126.572076 1
3.8%
126.571942 1
3.8%
126.571731 1
3.8%
126.571582 1
3.8%
126.571427 1
3.8%
126.571327 1
3.8%
126.571087 1
3.8%
126.57108 1
3.8%

title
Text

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-12T04:35:30.120682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length6.2307692
Min length2

Characters and Unicode

Total characters162
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)84.6%

Sample

1st row주차장
2nd row화장실
3rd row외부 관람로
4th row다리
5th row불로초 공원
ValueCountFrequency (%)
전망대 5
 
9.8%
입구 4
 
7.8%
매표소 3
 
5.9%
종료 3
 
5.9%
다리 3
 
5.9%
데크길 2
 
3.9%
전시관 2
 
3.9%
화장실 2
 
3.9%
시작 2
 
3.9%
주차장 2
 
3.9%
Other values (20) 23
45.1%
2023-12-12T04:35:31.417569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25
 
15.4%
전 7
 
4.3%
대 6
 
3.7%
로 5
 
3.1%
입 5
 
3.1%
망 5
 
3.1%
구 4
 
2.5%
리 4
 
2.5%
시 4
 
2.5%
장 4
 
2.5%
Other values (52) 93
57.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 134
82.7%
Space Separator 25
 
15.4%
Other Punctuation 3
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 7
 
5.2%
대 6
 
4.5%
로 5
 
3.7%
입 5
 
3.7%
망 5
 
3.7%
구 4
 
3.0%
리 4
 
3.0%
시 4
 
3.0%
장 4
 
3.0%
관 4
 
3.0%
Other values (49) 86
64.2%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
/ 1
33.3%
Space Separator
ValueCountFrequency (%)
25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 134
82.7%
Common 28
 
17.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 7
 
5.2%
대 6
 
4.5%
로 5
 
3.7%
입 5
 
3.7%
망 5
 
3.7%
구 4
 
3.0%
리 4
 
3.0%
시 4
 
3.0%
장 4
 
3.0%
관 4
 
3.0%
Other values (49) 86
64.2%
Common
ValueCountFrequency (%)
25
89.3%
, 2
 
7.1%
/ 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 134
82.7%
ASCII 28
 
17.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
25
89.3%
, 2
 
7.1%
/ 1
 
3.6%
Hangul
ValueCountFrequency (%)
전 7
 
5.2%
대 6
 
4.5%
로 5
 
3.7%
입 5
 
3.7%
망 5
 
3.7%
구 4
 
3.0%
리 4
 
3.0%
시 4
 
3.0%
장 4
 
3.0%
관 4
 
3.0%
Other values (49) 86
64.2%

Description
Text

MISSING 

Distinct16
Distinct (%)94.1%
Missing9
Missing (%)34.6%
Memory size340.0 B
2023-12-12T04:35:31.826844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length73
Median length27
Mean length17.176471
Min length2

Characters and Unicode

Total characters292
Distinct characters116
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)88.2%

Sample

1st row시작
2nd row 데크길 총 길이 21m 17m지점 오르막(7.5도) 내리막 1.5m 10도
3rd row10도 39m
4th row다리 길이 약 21m
5th row매표소 앞 경사로 8도 2m
ValueCountFrequency (%)
10도 3
 
3.9%
시작 3
 
3.9%
앞쪽 2
 
2.6%
계단 2
 
2.6%
판석길 2
 
2.6%
장애인 2
 
2.6%
다리 2
 
2.6%
나무 2
 
2.6%
데크길 2
 
2.6%
21m 2
 
2.6%
Other values (54) 55
71.4%
2023-12-12T04:35:32.452677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
51
 
17.5%
11
 
3.8%
11
 
3.8%
도 8
 
2.7%
m 8
 
2.7%
1 7
 
2.4%
이 7
 
2.4%
길 7
 
2.4%
, 5
 
1.7%
인 5
 
1.7%
Other values (106) 172
58.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 168
57.5%
Space Separator 51
 
17.5%
Decimal Number 26
 
8.9%
Control 22
 
7.5%
Lowercase Letter 9
 
3.1%
Other Punctuation 9
 
3.1%
Dash Punctuation 2
 
0.7%
Open Punctuation 2
 
0.7%
Close Punctuation 2
 
0.7%
Math Symbol 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 8
 
4.8%
이 7
 
4.2%
길 7
 
4.2%
인 5
 
3.0%
시 4
 
2.4%
장 3
 
1.8%
나 3
 
1.8%
쪽 3
 
1.8%
문 3
 
1.8%
용 3
 
1.8%
Other values (84) 122
72.6%
Decimal Number
ValueCountFrequency (%)
1 7
26.9%
3 3
11.5%
0 3
11.5%
2 3
11.5%
6 2
 
7.7%
8 2
 
7.7%
7 2
 
7.7%
5 2
 
7.7%
9 1
 
3.8%
4 1
 
3.8%
Other Punctuation
ValueCountFrequency (%)
, 5
55.6%
. 3
33.3%
/ 1
 
11.1%
Control
ValueCountFrequency (%)
11
50.0%
11
50.0%
Lowercase Letter
ValueCountFrequency (%)
m 8
88.9%
c 1
 
11.1%
Space Separator
ValueCountFrequency (%)
51
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 168
57.5%
Common 115
39.4%
Latin 9
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 8
 
4.8%
이 7
 
4.2%
길 7
 
4.2%
인 5
 
3.0%
시 4
 
2.4%
장 3
 
1.8%
나 3
 
1.8%
쪽 3
 
1.8%
문 3
 
1.8%
용 3
 
1.8%
Other values (84) 122
72.6%
Common
ValueCountFrequency (%)
51
44.3%
11
 
9.6%
11
 
9.6%
1 7
 
6.1%
, 5
 
4.3%
. 3
 
2.6%
3 3
 
2.6%
0 3
 
2.6%
2 3
 
2.6%
- 2
 
1.7%
Other values (10) 16
 
13.9%
Latin
ValueCountFrequency (%)
m 8
88.9%
c 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 168
57.5%
ASCII 124
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
51
41.1%
11
 
8.9%
11
 
8.9%
m 8
 
6.5%
1 7
 
5.6%
, 5
 
4.0%
. 3
 
2.4%
3 3
 
2.4%
0 3
 
2.4%
2 3
 
2.4%
Other values (12) 19
 
15.3%
Hangul
ValueCountFrequency (%)
도 8
 
4.8%
이 7
 
4.2%
길 7
 
4.2%
인 5
 
3.0%
시 4
 
2.4%
장 3
 
1.8%
나 3
 
1.8%
쪽 3
 
1.8%
문 3
 
1.8%
용 3
 
1.8%
Other values (84) 122
72.6%

Image
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing26
Missing (%)100.0%
Memory size366.0 B

Point
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.5
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T04:35:32.661799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.25
Q17.25
median13.5
Q319.75
95-th percentile24.75
Maximum26
Range25
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation7.6485293
Coefficient of variation (CV)0.56655772
Kurtosis-1.2
Mean13.5
Median Absolute Deviation (MAD)6.5
Skewness0
Sum351
Variance58.5
MonotonicityStrictly increasing
2023-12-12T04:35:32.846864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 1
 
3.8%
15 1
 
3.8%
26 1
 
3.8%
25 1
 
3.8%
24 1
 
3.8%
23 1
 
3.8%
22 1
 
3.8%
21 1
 
3.8%
20 1
 
3.8%
19 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
1 1
3.8%
2 1
3.8%
3 1
3.8%
4 1
3.8%
5 1
3.8%
6 1
3.8%
7 1
3.8%
8 1
3.8%
9 1
3.8%
10 1
3.8%
ValueCountFrequency (%)
26 1
3.8%
25 1
3.8%
24 1
3.8%
23 1
3.8%
22 1
3.8%
21 1
3.8%
20 1
3.8%
19 1
3.8%
18 1
3.8%
17 1
3.8%

info
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
<NA>
14 
1
12 

Length

Max length4
Median length4
Mean length2.6153846
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 14
53.8%
1 12
46.2%

Length

2023-12-12T04:35:33.056890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:35:33.235887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 14
53.8%
1 12
46.2%

A
Categorical

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
A
26 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 26
100.0%

Length

2023-12-12T04:35:33.390541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:35:33.526426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 26
100.0%

B
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
B
16 
<NA>
10 

Length

Max length4
Median length1
Mean length2.1538462
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
B 16
61.5%
<NA> 10
38.5%

Length

2023-12-12T04:35:33.661022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T04:35:33.781115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
b 16
61.5%
na 10
38.5%

etc
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing22
Missing (%)84.6%
Memory size340.0 B
2023-12-12T04:35:33.965399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length15
Mean length10.5
Min length6

Characters and Unicode

Total characters42
Distinct characters33
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row접근성정보:1(*계단, 길 상태, 경사)
2nd row장애인화장실:2
3rd row전체코스:A
4th row추천코스:B
ValueCountFrequency (%)
접근성정보:1(*계단 1
14.3%
길 1
14.3%
상태 1
14.3%
경사 1
14.3%
장애인화장실:2 1
14.3%
전체코스:a 1
14.3%
추천코스:b 1
14.3%
2023-12-12T04:35:34.465062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 4
 
9.5%
3
 
7.1%
장 2
 
4.8%
, 2
 
4.8%
스 2
 
4.8%
코 2
 
4.8%
애 1
 
2.4%
인 1
 
2.4%
화 1
 
2.4%
실 1
 
2.4%
Other values (23) 23
54.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26
61.9%
Other Punctuation 7
 
16.7%
Space Separator 3
 
7.1%
Decimal Number 2
 
4.8%
Uppercase Letter 2
 
4.8%
Close Punctuation 1
 
2.4%
Open Punctuation 1
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 2
 
7.7%
스 2
 
7.7%
코 2
 
7.7%
애 1
 
3.8%
인 1
 
3.8%
화 1
 
3.8%
실 1
 
3.8%
접 1
 
3.8%
체 1
 
3.8%
추 1
 
3.8%
Other values (13) 13
50.0%
Other Punctuation
ValueCountFrequency (%)
: 4
57.1%
, 2
28.6%
* 1
 
14.3%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
1 1
50.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 26
61.9%
Common 14
33.3%
Latin 2
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 2
 
7.7%
스 2
 
7.7%
코 2
 
7.7%
애 1
 
3.8%
인 1
 
3.8%
화 1
 
3.8%
실 1
 
3.8%
접 1
 
3.8%
체 1
 
3.8%
추 1
 
3.8%
Other values (13) 13
50.0%
Common
ValueCountFrequency (%)
: 4
28.6%
3
21.4%
, 2
14.3%
2 1
 
7.1%
) 1
 
7.1%
* 1
 
7.1%
( 1
 
7.1%
1 1
 
7.1%
Latin
ValueCountFrequency (%)
A 1
50.0%
B 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 26
61.9%
ASCII 16
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 4
25.0%
3
18.8%
, 2
12.5%
2 1
 
6.2%
) 1
 
6.2%
A 1
 
6.2%
* 1
 
6.2%
( 1
 
6.2%
1 1
 
6.2%
B 1
 
6.2%
Hangul
ValueCountFrequency (%)
장 2
 
7.7%
스 2
 
7.7%
코 2
 
7.7%
애 1
 
3.8%
인 1
 
3.8%
화 1
 
3.8%
실 1
 
3.8%
접 1
 
3.8%
체 1
 
3.8%
추 1
 
3.8%
Other values (13) 13
50.0%

Interactions

2023-12-12T04:35:27.979839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:26.772557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:27.125667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:27.521523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:28.084049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:26.852590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:27.216990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:27.617672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:28.196429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:26.962595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:27.317422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:27.719507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:28.295182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:27.050585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:27.427846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T04:35:27.879178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T04:35:34.777667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
IDlatlontitleDescriptionPointetc
ID1.0000.8210.7090.8540.9331.0001.000
lat0.8211.0000.6670.9731.0000.8211.000
lon0.7090.6671.0000.9881.0000.7091.000
title0.8540.9730.9881.0001.0000.8541.000
Description0.9331.0001.0001.0001.0000.933NaN
Point1.0000.8210.7090.8540.9331.0001.000
etc1.0001.0001.0001.000NaN1.0001.000
2023-12-12T04:35:35.069160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
infoB
info1.0001.000
B1.0001.000
2023-12-12T04:35:35.241323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
IDlatlonPointinfoB
ID1.000-0.696-0.3591.0001.0001.000
lat-0.6961.0000.650-0.6961.0001.000
lon-0.3590.6501.000-0.3591.0001.000
Point1.000-0.696-0.3591.0001.0001.000
info1.0001.0001.0001.0001.0001.000
B1.0001.0001.0001.0001.0001.000

Missing values

2023-12-12T04:35:28.461483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T04:35:28.679322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T04:35:28.849657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDlatlontitleDescriptionImagePointinfoABetc
0133.245236126.571064주차장<NA><NA>1<NA>AB접근성정보:1(*계단, 길 상태, 경사)
1233.245478126.571327화장실<NA><NA>2<NA>AB장애인화장실:2
2333.245136126.57108외부 관람로시작<NA>3<NA>AB전체코스:A
3433.245093126.571427다리<NA><NA>4<NA>A<NA>추천코스:B
4533.245116126.571731불로초 공원<NA><NA>5<NA>A<NA><NA>
5633.245419126.571942테크길 입구데크길 총 길이 21m 17m지점 오르막(7.5도) 내리막 1.5m 10도<NA>61A<NA><NA>
6733.245412126.572076오르막 종료, 내리막 시작10도 39m<NA>71A<NA><NA>
7833.245368126.571582아치형 다리다리 길이 약 21m<NA>8<NA>A<NA><NA>
8933.245182126.571066야외 관람로 종료<NA><NA>9<NA>A<NA><NA>
91033.244946126.570522매표소 진입로<NA><NA>10<NA>AB<NA>
IDlatlontitleDescriptionImagePointinfoABetc
161733.244544126.570754전망대<NA><NA>171A<NA><NA>
171833.24438126.570635나무 데크길 시작판석길 끝, 나무 데크길 시작<NA>181AB<NA>
181933.244358126.570187전망대 앞정방폭포, 4.3학살터가 보임<NA>191AB<NA>
192033.244261126.570032반환점/나무 데크길 종료앞쪽 계단<NA>201AB<NA>
202133.244257126.569898야외무대, 전망대앞쪽 계단<NA>211A<NA><NA>
212233.244478126.570114연못 다리나무 데크 다리<NA>221AB<NA>
222333.24455126.570427서복 공원 입구<NA><NA>23<NA>AB<NA>
232433.244608126.573229정방폭포 전망대장애인, 보조기기 이용인, 노약자를 위한 전망대<NA>24<NA>AB<NA>
242533.24465126.573356정방폭포 매표소<NA><NA>25<NA>AB<NA>
252633.245236126.571064주차장도착<NA>26<NA>AB<NA>